![]() ![]() Average error rates calculated using the MRE formula ranged from 85% to 772%, with many falling in the 500-600% range. ![]() First, models developed in different environments did not perform very well uncalibrated, as might be expected. The majority of projects were written in COBOL, with an average size of approximately 200,000 source lines of code.Īnalysis of the data yielded several practical results. The fifteen projects collected for this study covered a range of profit and not-for-profit applications. The source of the project data was a national computer consulting and services firm specializing in the design and development of data processing systems. The latter test was used for calibration and to judge the relative goodness of fit of the resulting linear models. The second test was to run simple regressions with the estimate as the independent variable and the actual effort as the dependent variable. ![]() The first was Conte's Magnitude of Relative Error (MRE) test, which divides the difference between the estimate and the actual effort by the actual effort, then takes the absolute value to eliminate problems with averaging positive and negative variances. Two tests were used to assess the accuracy of these models. Are the models that are in the open literature as accurate as the proprietary models, thus eliminating the need to purchase estimating software? The methodology for evaluating these models was to gather data on completed software development projects and compare the actual costs with the ex post estimates obtained from the four models. Can the latter models be as accurate as the SLOC models, thus eliminating the need to attempt to estimate lines of code early in the project? 3) Two of the models are proprietary and two are not. Specifically, this paper addresses the following questions: 1) Are these models accurate outside their original environments and can they be easily calibrated? 2) Two of the models use source lines of code (SLOC) as an input, and two use inputs that are easier to estimate early in the project life cycle. This paper evaluates four of the most popular algorithmic models used to estimate software costs (SLIM, COCOMO, FUNCTION POINTS, and ESTIMACS). As a result, considerable research attention is now directed at gaining a better understanding of the software development process, as well as constructing and evaluating software cost estimating tools. This concern has become even more pressing as these costs continue to increase. It is the advanced model that estimates the software development effort like Intermediate COCOMO in each stage of the software development life cycle process.Practitioners have expressed concern over their inability to accurately estimate costs associated with software development. ![]() The estimated effort and scheduled time are given by the relationship:ĮAF = It is an Effort Adjustment Factor, which is calculated by multiplying the parameter values of different cost driver parameters. The intermediate model estimates software development effort in terms of size of the program and other related cost drivers parameters (product parameter, hardware parameter, resource parameter, and project parameter) of the project.
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